Verification and Validation in Scientific Computing Can you trust results from modelling and simulation? Verification, validation, and uncertainty quantification can help. William L. Oberkampf (Author), Christopher J. Roy (Author) 9780521113601, Cambridge University Press Hardback, published 14 October 2010 790 pages, 220 b/w illus. 14 colour illus. 55 tables25.4 x 17.9 x 4 cm, 1.68 kg 'This book provides a comprehensive and systematic development of basic concepts and procedures for verification and validation of models and simulations.' Zentralblatt MATH Advances in scientific computing have made modelling and simulation an important part of the decision-making process in engineering, science, and public policy. This book provides a comprehensive and systematic development of the basic concepts, principles, and procedures for verification and validation of models and simulations. The emphasis is placed on models that are described by partial differential and integral equations and the simulations that result from their numerical solution. The methods described can be applied to a wide range of technical fields, from the physical sciences, engineering and technology and industry, through to environmental regulations and safety, product and plant safety, financial investing, and governmental regulations. This book will be genuinely welcomed by researchers, practitioners, and decision makers in a broad range of fields, who seek to improve the credibility and reliability of simulation results. It will also be appropriate either for university courses or for independent study. Preface 1. Introduction Part I. Fundamental Concepts: 2. Fundamental concepts and terminology 3. Modeling and computational simulation Part II. Code Verification: 4. Software engineering 5. Code verification 6. Exact solutions Part III. Solution Verification: 7. Solution verification 8. Discretization error 9. Solution adaptation Part IV. Model Validation and Prediction: 10. Model validation fundamentals 11. Design and execution of validation experiments 12. Model accuracy assessment 13. Predictive capability Part V. Planning, Management, and Implementation Issues: 14. Planning and prioritization in modeling and simulation 15. Maturity assessment of modeling and simulation 16. Development and responsibilities for verification, validation and uncertainty quantification Appendix. Programming practices Index. Subject Areas: Maths for computer scientists [UYAM], Numerical analysis [PBKS], Differential calculus & equations [PBKJ]
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